A benchmark dataset and workflow for landslide susceptibility zonation

• Published in: Earth-science reviews Vol. 258; p. 104927
• Main Authors: Alvioli, Massimiliano, Loche, Marco, Jacobs, Liesbet, Grohmann, Carlos H., Abraham, Minu Treesa, Gupta, Kunal, Satyam, Neelima, Scaringi, Gianvito, Bornaetxea, Txomin, Rossi, Mauro, Marchesini, Ivan, Lombardo, Luigi, Moreno, Mateo, Steger, Stefan, Camera, Corrado A.S., Bajni, Greta, Samodra, Guruh, Wahyudi, Erwin Eko, Susyanto, Nanang, Sinčić, Marko, Gazibara, Sanja Bernat, Sirbu, Flavius, Torizin, Jewgenij, Schüßler, Nick, Mirus, Benjamin B., Woodard, Jacob B., Aguilera, Héctor, Rivera-Rivera, Jhonatan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2024
• Subjects: Benchmark dataset; data collection; Geomorphological mapping; Geomorphometry; humans; Italy; Landslide inventory; Landslide susceptibility; Landslide susceptibility mapping; landslides; Machine learning; quantitative analysis; Slope units; Spatial analysis; Statistical modeling; Italy; Benchmark dataset; Statistical modeling; Landslide inventory; Landslide susceptibility mapping; Machine learning; Spatial analysis; Geomorphometry; Landslide susceptibility; Slope units; Geomorphological mapping

Landslide susceptibility shows the spatial likelihood of landslide occurrence in a specific geographical area and is a relevant tool for mitigating the impact of landslides worldwide. As such, it is the subject of countless scientific studies. Many methods exist for generating a susceptibility map,...